Intelligent right of way determination for autonomous vehicles

ABSTRACT

Various technologies described herein pertain to generating a bid for turn priority at an intersection. An autonomous vehicle determines that the autonomous vehicle and a second autonomous vehicle are proximate to an intersection. The autonomous vehicle generates a first bid that is indicative of a first importance that the autonomous vehicle traverses the intersection. The first bid is based upon characteristics of a trip of a passenger riding in the autonomous vehicle. The autonomous vehicle transmits the first bid to a networked computing system, wherein the networked computing system determines a turn order based upon the first bid and a second bid generated by the second autonomous vehicle. The networked computing system transmits the turn order to the autonomous vehicle, wherein the autonomous vehicle operates based upon the turn order.

BACKGROUND

An autonomous vehicle is a motorized vehicle that can operate withouthuman conduction. An exemplary autonomous vehicle includes a pluralityof sensor systems, such as, but not limited to, a lidar sensor system, acamera sensor system, and a radar sensor system, amongst others. Theautonomous vehicle operates based upon sensor signals output by thevarious sensor systems.

The autonomous vehicle may utilize the sensor signals output by thesensor systems to aid in determining motion of the autonomous vehicle.Conventional motion planning techniques of autonomous vehicles tend tobe computationally intensive and constrained to right of way rules ofthe road. For example, when the autonomous vehicle reaches anintersection along with a second vehicle, the autonomous vehicleutilizes the sensor signals generated by the sensor systems in order todetermine whether the autonomous vehicle or the second vehicle has aright of way. For instance, the autonomous vehicle may give priority tothe second vehicle at the intersection when the second vehicle arrivesat the intersection prior the autonomous vehicle arriving at theintersection.

Conventional motion planning techniques of autonomous vehicles areassociated with various deficiencies. First, conventional motionplanning techniques tend to be computationally burdensome, as anautonomous vehicle typically processes a large amount of sensor signalsgenerated by the sensor systems in order to determine whether theautonomous vehicle has priority at an intersection. Second, in certainscenarios, occlusions in driving environments may prevent the sensorsystems of the autonomous vehicle from perceiving certain areas of adriving environment of the autonomous vehicle, which may negativelyaffect determining whether the autonomous vehicle has priority at theintersection. Third, conventional motion planning techniques rely uponconventional right of way rules of the road, which may not always beefficient or desirable, especially in driving environments in which manyof the vehicles in the driving environment are autonomous vehicles.

SUMMARY

The following is a brief summary of subject matter that is described ingreater detail herein. This summary is not intended to be limiting as tothe scope of the claims.

Described herein are various technologies that pertain to controllingmotion planning of an autonomous vehicle when the autonomous vehicle isat an intersection. With more specificity, described herein are varioustechnologies pertaining to generating a bid for turn order priority foran autonomous vehicle located at an intersection.

According to various embodiments, an autonomous vehicle comprises avehicle propulsion system, a braking system, a steering system, and aplurality of sensor systems. The plurality of sensor systems generate aplurality of sensor signals that are indicative of a driving environmentof the autonomous vehicle. In an example, the driving environmentincludes an intersection (e.g., an intersection having a four-way stopsign) and a second autonomous vehicle. The autonomous vehicle furthercomprises a computing system that is in communication with the pluralityof sensor systems, the vehicle propulsion system, the braking system,and the steering system.

In operation, it is contemplated that the autonomous vehicle and thesecond autonomous vehicle are approaching the intersection. Thecomputing system of the autonomous vehicle can determine that theautonomous vehicle and the second autonomous vehicle are proximate tothe intersection based upon the plurality of sensor signals. Accordingto another example, it is contemplated that a networked computing systemcan determine that the autonomous vehicle and the second autonomousvehicle are proximate to the intersection.

The autonomous vehicle generates a first bid for turn priority at theintersection. The first bid is indicative of a first importance that theautonomous vehicle traverses the intersection. The first bid is basedupon characteristics of a trip of a passenger riding in the autonomousvehicle. Alternatively, when the autonomous vehicle is not transportinga passenger, the first bid may be based solely on characteristics of atrip of the autonomous vehicle.

The characteristics of the trip of the passenger may include at leastone of an intended path of the autonomous vehicle that creates ashortest distance of the trip, a priority of the autonomous vehicle, apriority of the passenger, an amount of idle time the autonomous vehiclehas spent at the intersection, a ride quality experienced by thepassenger, a total time of the trip, an expected arrival time of theautonomous vehicle at a destination, previous approval ratings of anautonomous vehicle service that is responsible for the autonomousvehicle, energy consumption of the autonomous vehicle, fuel consumptionof the autonomous vehicle, remaining distance left on the trip, or atype of the trip. In an embodiment, some or all of the characteristicsof the trip may be weighted.

The second autonomous vehicle also generates a second bid for turnpriority. The second bid is indicative of a second importance that thesecond autonomous vehicle traverses the intersection. The second bid isbased upon second characteristics of a second trip of a second passengerriding in the second autonomous vehicle. The second characteristics maybe similar to the above-identified characteristics.

The computing system of the autonomous vehicle transmits the first bidto a networked computing system. The second autonomous vehicle alsotransmits the second bid to the networked computing system. Thenetworked computing system receives the first bid and the second bid.The networked computing system determines a turn order (i.e., whichautonomous vehicle is to traverse the intersection first) of theautonomous vehicle and the second autonomous vehicle based upon thefirst bid and the second bid. More specifically, the networked computingsystem determines the turn order based upon the characteristics and thesecond characteristics. In an example, the autonomous vehicle may havearrived at the intersection subsequent to the second autonomous vehicle,but the turn order may indicate that the autonomous vehicle is totraverse the intersection prior to the second autonomous vehicletraversing the intersection.

The networked computing system further transmits the turn order to theautonomous vehicle as well as the second autonomous vehicle. Responsiveto receiving the turn order, the computing system of the autonomousvehicle controls at least one of the vehicle propulsion system, thebraking system, or the steering system based upon the turn order.Additionally, the second autonomous vehicle also operates based upon theturn order.

The above-described technologies present various advantages overconventional motion planning technologies. First, through use of the bidsystem described above, the above-described technologies may reduce theamount of computing resources utilized by the autonomous vehicle indetermining when the autonomous vehicle is to traverse an intersection.Second, the above-described technologies facilitate a more efficientflow of traffic that is not bound by conventional right of way rules.

The above summary presents a simplified summary in order to provide abasic understanding of some aspects of the systems and/or methodsdiscussed herein. This summary is not an extensive overview of thesystems and/or methods discussed herein. It is not intended to identifykey/critical elements or to delineate the scope of such systems and/ormethods. Its sole purpose is to present some concepts in a simplifiedform as a prelude to the more detailed description that is presentedlater.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a functional block diagram of a networked environmentincluding an exemplary autonomous vehicle.

FIG. 2A illustrates a functional block diagram of an exemplaryautonomous vehicle.

FIG. 2B illustrates a functional block diagram of a computing system ofthe autonomous vehicle of FIG. 2 in accordance with various examples.

FIG. 3 illustrates a functional block diagram of an exemplary networkedcomputing system.

FIG. 4 illustrates an exemplary accumulated occupancy grid for a giventime.

FIG. 5A illustrates an exemplary driving environment of autonomousvehicles.

FIG. 5B illustrates exemplary driving environment of autonomousvehicles.

FIG. 6 is a flow diagram that illustrates an exemplary methodologyexecuted by an autonomous vehicle for generating a bid for turn priorityat an intersection.

FIG. 7 is a flow diagram that illustrates an exemplary methodologyexecuted by an autonomous vehicle for removing an occlusion from anoccupancy grid.

FIG. 8 is a flow diagram that illustrates an exemplary methodologyexecuted by a networked computing system for generating a turn orderfrom bids received from multiple autonomous vehicles.

FIG. 9 illustrates an exemplary computing device.

DETAILED DESCRIPTION

Various technologies pertaining to determining a turn order of multipleautonomous vehicles and/or querying surrounding autonomous vehicles forcached objects and/or fields of view as part of motion planning for anautonomous vehicle are now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of one or more aspects. It may be evident, however, thatsuch aspect(s) may be practiced without these specific details. In otherinstances, well-known structures and devices are shown in block diagramform in order to facilitate describing one or more aspects. Further, itis to be understood that functionality that is described as beingcarried out by certain system components may be performed by multiplecomponents. Similarly, for instance, a component may be configured toperform functionality that is described as being carried out by multiplecomponents.

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B. In addition, the articles “a” and“an” as used in this application and the appended claims shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from the context to be directed to a singular form.

As used herein, the terms “component” and “system” are intended toencompass computer-readable data storage that is configured withcomputer-executable instructions that cause certain functionality to beperformed when executed by a processor. The computer-executableinstructions may include a routine, a function, or the like. It is alsoto be understood that a component or system may be localized on a singledevice or distributed across several devices. Further, as used herein,the term “exemplary” is intended to mean “serving as an illustration orexample of something.”

Referring now to the drawings, FIG. 1 illustrates a networkedenvironment of multiple autonomous vehicles 1 100 to N 101, namely anautonomous vehicle 1 100, . . . , and an autonomous vehicle N 101, whereN can be substantially any integer greater than 1. In the illustratedexample, a networked computing system 130 is in communication with theautonomous vehicles 100-101 via a network 128. In an embodiment, thenetworked computing system 130 may be a server computing device or acloud computing system. The network 128 includes a communication link129. The communication link 129 can use various communication protocols,such as wireless communications, cellular communications, IEEE 802.11(Wi-Fi), Long Term Evolution (LTE), Time Division Multiplex (TDM),asynchronous transfer mode (ATM), Internet Protocol (IP), Ethernet,synchronous optical networking (SONET), hybrid fiber-coax (HFC),circuit-switched, communication signaling, or some other communicationformat, including combinations, improvements, or variations thereof.Communication links can each be a direct link or can includeintermediate networks, systems, or devices, and can include a logicalnetwork link transported over multiple physical links.

Although a single communication link is shown in FIG. 1 betweenparticular elements, it should be understood that this is merelyillustrative to show communication modes or access pathways. In otherexamples, further links can exist, with portions of the further linksshared and used for different communication sessions or differentcontent types, among other configurations. Communication links can eachinclude many different signals sharing the same associated link, suchas: resource blocks, access channels, paging channels, notificationchannels, forward links, reverse links, user communications,communication sessions, overhead communications, carrier frequencies,other channels, timeslots, spreading codes, transportation ports,logical transportation links, network sockets, packets, or communicationdirections.

Referring now to FIG. 2A, the exemplary autonomous vehicle 1 100 that ispart of the networked environment described in FIG. 1 is illustrated.The autonomous vehicle 1 100 can navigate about roadways without humanconduction based upon sensor signals outputted by sensor systems of theautonomous vehicle 1 100. The autonomous vehicle 1 100 includes aplurality of sensor systems, namely, a sensor system 1 102, . . . , anda sensor system N 104, where N can be substantially any integer greaterthan 1 (collectively referred to herein as sensor systems 102-104). Thesensor systems 102-104 are of different types and are arranged about theautonomous vehicle 1 100. For example, the sensor system 1 102 may be alidar sensor system and the sensor system N 104 may be a camera (image)sensor system. Other exemplary sensor systems included in the sensorsystems 102-104 can include radar sensor systems, global positioningsystem (GPS) sensor systems, sonar sensor systems, infrared sensorsystems, and the like.

The autonomous vehicle 1 100 further includes several mechanical systemsthat are used to effectuate appropriate motion of the autonomous vehicle1 100. For instance, the mechanical systems can include, but are notlimited to, an vehicle propulsion system 106, a braking system 108, anda steering system 110. The vehicle propulsion system 106 may be anelectric motor, an internal combustion engine, or a combination thereof.The braking system 108 can include an engine brake, brake pads,actuators, and/or any other suitable componentry that is configured toassist in decelerating the autonomous vehicle 1 100. The steering system110 includes suitable componentry that is configured to control thedirection of movement of the autonomous vehicle 1 100.

The autonomous vehicle 1 100 additionally includes a computing system112 that is in communication with the sensor systems 102-104, thevehicle propulsion system 106, the braking system 108, and the steeringsystem 110. The computing system 112 includes a network interface 113, aprocessor 114, and memory 116. The memory 116 includescomputer-executable instructions that are executed by the processor 114.

Pursuant to various examples, the processor 114 can be or include agraphics processing unit (GPU), a plurality of GPUs, a centralprocessing unit (CPU), a plurality of CPUs, an application-specificintegrated circuit (ASIC), a microcontroller, a programmable logiccontroller (PLC), a field programmable gate array (FPGA), or the like.

The network interface 113 enables the autonomous vehicle 100 1 tocommunicate over the network 128. In an example, the autonomous vehicle100 1 may communicate with the autonomous vehicle 101 N over the network128 using the network interface 113. In another example, the autonomousvehicle 100 1 may communicate with the networked computing system 130using the network interface 113. The Network interface 113 may usevarious wired and wireless connection protocols such as, direct connect,Ethernet, Bluetooth®, IEEE 1394a-b, serial, universal serial bus (USB),Digital Visual Interface (DVI), 802.11a/b/g/n/x, cellular, Miracast, andthe like.

The computing system 112 can further include a data store 119. The datastore 119 may include a prior sensor space map 120 that includes priordata for geographic locations in a driving environment corresponding towhether predefined static objects are located at the geographiclocations. In an example, the prior sensor space map 120 may be basedupon radar signals. The predefined static objects may be buildings orobjects that can reflect radar signals. Moreover, the predefined staticobjects may be at geographic locations that are in paths of autonomousvehicles, such as the autonomous vehicle 1 100. The prior data in radaroutputs corresponding to the predefined static objects can yield falsepositives or obscured results, which can detrimentally impact operationof the autonomous vehicle 1 100. For example, the autonomous vehicle 1100 may stop due to incorrectly equating the radar data corresponding tothe manhole cover as a car.

The prior sensor space map 120 may further include known occlusions indriving environments that are blocked from radar or lidar that may bepotentially hazardous to the autonomous vehicle 1 100 without furtherinformation. While many of the examples set forth herein describe thedata store 119 of the autonomous vehicle 1 100 as including the priorsensor space map 120, it is contemplated that a data store of thenetworked computing system 130 can additionally or alternatively includethe prior sensor space map 120.

Pursuant to another example, it is to be appreciated that the data store119 of the autonomous vehicle 1 100 may include a portion of the priorsensor space map 120 (e.g., the autonomous vehicle 1 100 can receive theportion of the prior sensor space map 120 from the networked computingsystem 130 or from the autonomous vehicle N 101 in the surrounding area)to aid in predicting future occlusions. The autonomous vehicle 1 100 maygenerate the prior sensor space map 120 as an occupancy grid bycollecting sensor signals output by the sensor systems 102-104 over timeand identifying the predefined static object that are occlusions in thesensor signals. This is convenient for determining occlusions, such as,buildings, but may not be effective for stalled vehicles on the road, asan example.

The memory 116 of the computing system 112 includes an object detectionsystem 118. The object detection system 118 can retrieve prior data forthe geographic location in the driving environment of the autonomousvehicle 1 100 from the prior sensor space map 120. If the objectdetection system 118 identifies a predefined static object or a realtime static object as an occlusion to sensor signals at the geographiclocation (e.g., as specified in the prior sensor space map 120), thenthe prior data for the geographic location can aid in reconfiguring anoccupancy grid from the sensor signals of another autonomous vehicle inthe geographic location, as will be further discussed below.

The memory 116 additionally includes a control system 122. The controlsystem 122 is configured to control at least one of the mechanicalsystems of the autonomous vehicle 1 100 (e.g., at least one of thevehicle propulsion system 106, the braking system 108, and/or thesteering system 110). For instance, the control system 122 can controlthe vehicle propulsion system 106, the braking system 108, and/or thesteering system 110 based on a motion plan generated by the computingsystem 112 or a turn order plan for the autonomous vehicle 1 100generated by the networked computing system 130, as will be furtherdiscussed below.

Now turning to FIG. 2B, illustrated is the computing system 112 of theautonomous vehicle 1 100 of FIG. 2A according to various embodiments.The computing system 112 again includes the processor 114 and the memory116. The memory 116 can include the object detection system 118, thecontrol system 122, and further can include a tracking system 140, anoccupancy accumulator system 142, an occlusion aid system 144, and a bidgeneration system 146.

The memory 116 can include a tracking system 140. The tracking system140 can track an object in a driving environment surrounding theautonomous vehicle 1 100 based on sensor signals outputted by the sensorsystems 102-104 of the autonomous vehicle 1 100 for a given time (inreal time). Further, the object tracked in the driving environment canbe referred to as a tracked object. The tracked object can be, forinstance, a vehicle, another autonomous vehicle, a truck, a bus, a bike,a pedestrian, or the like. The tracking system 140 can identify wherethe object is located in the driving environment surrounding theautonomous vehicle 1 100 based on the sensor signals outputted by thesensor system 102-104 for the given time. Moreover, the tracking system140 can determine a speed at which the object is moving, a direction ofmovement of the object, and so forth. While one object is described asbeing tracked, it is contemplated that the tracking system 140 may tracksubstantially any number of objects in the driving environmentsurrounding the autonomous vehicle 1 100. The tracking system 140 mayalso be used to determine that a second autonomous vehicle N 101 is inproximity to an intersection.

Moreover, the memory 116 of the computing system 112 can include anoccupancy accumulator system 142. The occupancy accumulator system 142can generate an accumulated occupancy grid for a given time based atleast in part on the data outputted by the sensor systems 102-104 forthe given time. The occupancy accumulator system 142 can remove thetracked object(s) in the driving environment to generate the accumulatedoccupancy grid for the given time. The occupancy accumulator system 142can determine the layout of the road including upcoming intersectionsand/or crossways. The occupancy accumulator system 142 can determinewhen the autonomous vehicle is within a predetermined proximity to anintersection (e.g., 500 ft, 100 ft, 50 ft, 10 ft).

Moreover, the occupancy accumulator system 142 can further generate theaccumulated occupancy grid for the given time. The grid can specify anundrivable area in the driving environment surrounding the autonomousvehicle 1 100. Thus, for instance, the accumulated occupancy grid caninclude at least one cell that signifies the undrivable area in thedriving environment. The occupancy accumulator system 142 can alsodetect an unknown area or occlusion in the driving environmentsurrounding the autonomous vehicle 1 100 based on the data outputted bythe sensor systems 102-104 for the given time. The unknown area in thedriving environment, for instance, can be occluded from a perspective ofa sensor system in the sensor systems 102-104. Accordingly, theoccupancy accumulator system 142 can generate the accumulated occupancygrid for the given time, such that at least one cell signifies theunknown area in the driving environment.

The memory 116 of the computing system 112 includes an occlusion aidsystem 144. According to an illustration, one or more of the sensorsystems 102-104 can output sensor signals that are indicative of thedriving environment surrounding the autonomous vehicle 1 100. Followingthis illustration, the occupancy accumulator system 142 can generate anoccupancy grid based on the sensor signals outputted by the one or moresensor systems 102-104 or the object detection system 118 can generatean occupancy grid based on the prior data known at a certain geographiclocation in the driving environment.

Once the occupancy accumulator system 142 determines that an occlusionexists along a travelled path of the autonomous vehicle 1 100, theocclusion aid system 144 of the autonomous vehicle 1 100 may transmit aquery to the autonomous vehicle N 101 in the driving environment. As anexample, the occlusion aid system 144 the query may be indicative ofwhether particular cells of an occupancy grid are blocked by anocclusion (the occupancy grid created by either the object detectionsystem 118 or the occupancy accumulator system 142). The particularcells for which the occlusion aid system 144 performs the query canrepresent the space in the occupancy grid past the occlusion todetermine if there is an object (e.g. on-coming traffic). Thisinformation may be queried directly from the autonomous vehicle N 101 orfrom the networked computing system 130 (or another computing system).In an embodiment, a transmission protocol confirms the connectionbetween the occlusion aid system 144 and the networked computing system130. Either the networked computing system 130 acts as a proxy to theautonomous vehicle N 101 or the autonomous vehicle N 101 itself maytransmit sensor signals output from sensor systems of the autonomousvehicle N 101 that included the requested cells.

The query may be a short-range transmission so that only the autonomousvehicle N 101 in unknown areas of the driving environment can respond.The unknown areas are transmitted with the query as data (e.g. packets)and the autonomous vehicle N 101 can use its sensor signals output bysensor systems of the autonomous vehicle N 101 to aid in correcting theobstructed view of the autonomous vehicle 1 100. An occlusion aid systemof the autonomous vehicle N 101 transmits the appropriate sensor signalsto reveal the obstructed area or the area surrounding it for autonomousvehicle 1 100. The occlusion aid system 114 of the autonomous vehicle 1100 may work with the occupancy accumulator system 142 to create a fulloccupancy grid including the previously unknown area.

In another example, the query may be transmitted to the networkedcomputing system 130. The networked computing system 130 may be bettersuited to determine that the autonomous vehicle N 101 is better able toprovide the autonomous vehicle 1 100 with sensor signals for the unknownarea. This may aid in selecting a single autonomous vehicle to respondinstead of numerous autonomous vehicles, unless more than one autonomousvehicle is necessary to completely reveal the obstructed area.

The memory 116 of the computing system 112 includes a bid generationsystem 146. The bid generation system 146 can generate a bid for turnpriority of the autonomous vehicle 1 100 at an intersection. The bidgeneration system 146 can generate the bid for turn priority at theintersection, where the bid is indicative of a first importance that theautonomous vehicle 100 traverses the intersection. Moreover, the bidgeneration system 146 can generate the bid based upon characteristics ofa trip of a passenger riding in the autonomous vehicle 100.

Now turning to FIG. 3, illustrated is an exemplary networked computingsystem 130 in accordance with various embodiments. The networkedcomputing system 130 can include a processor 314, memory 316, and anetwork interface 318. The memory 316 can include a turn orderdetermination system 320 and an occlusion aid query system 322.

Pursuant to various examples, the processor 314 can be or include agraphics processing unit (GPU), a plurality of GPUs, a centralprocessing unit (CPU), a plurality of CPUs, an application-specificintegrated circuit (ASIC), a microcontroller, a programmable logiccontroller (PLC), a field programmable gate array (FPGA), or the like.

Network interface 318 enables the networked computing system 130 tocommunicate over the network 128 with the autonomous vehicle 1 100 andthe autonomous vehicle N 101. Network interface 318 may use variouswired and wireless connection protocols such as, direct connect,Ethernet, Bluetooth®, IEEE 1394a-b, serial, universal serial bus (USB),Digital Visual Interface (DVI), 802.11a/b/g/n/x, cellular, miracast, andthe like.

The memory 316 of the networked computing system 130 includes a turnorder determination system 320. As will be described in greater detailbelow, the turn order determination system 320 can receive bids for turnpriority at the intersection from different autonomous vehicles at agiven location (e.g. proximate to an intersection) and can generate aturn order (for the intersection) based on the bids.

The memory 316 of the networked computing system 130 includes anocclusion aid query system 322. According to an example, the autonomousvehicle 1 100 can submit to the networked computing system 130 a queryfor particular cells of a particular occupancy grid (e.g. cells blockedby an occlusion). The networked computing system 130 may transmit arequest to the autonomous vehicle N 101 in the area to transmit to theautonomous vehicle 1 100 the particular cells in question. The nearestautonomous vehicle N 101 may not be in proximate to the particular cells(e.g. 10 ft, 25 ft, or 50 ft away). The nearest autonomous vehicle N 101may be passing that area at a given moment in time. Ideally, the nearestautonomous vehicle N 101 is proximate to the queried particular cellsand can continuously feed sensor signals indicative of those particularcells. It is foreseen that the autonomous vehicle 1 100 and theautonomous vehicle N 101 may not be in direct communication with oneanother. In this case, the occlusion aid query system 322 may transmitsensor signals output by sensor systems of the autonomous vehicle N 101to the autonomous vehicle 1 100 to fill in the occupancy grid for thequeried cells. It is envisioned that if the autonomous vehicle N 101 isnot be able to attain all of the queried cells, additional autonomousvehicles may also be queried.

Operation of the autonomous vehicle 1 100 and the networked computingsystem 130 is now set forth. It is contemplated that autonomous vehicle1 100 and the autonomous vehicle N 101 are approaching an intersection.The computing system of the autonomous vehicle determines thatautonomous vehicle 1 100 and the second autonomous vehicle are proximateto the intersection based upon a plurality of sensor signals generatedby the sensor systems 102-104 of the autonomous vehicle 1 100. Likewise,a computing system of the autonomous vehicle N 101 also determines thatthe autonomous vehicle 1 100 and the autonomous vehicle N 101 areproximate to the intersection based upon a plurality of sensor signalsgenerated by a plurality of sensor systems of the autonomous vehicle N101.

The autonomous vehicle 1 100 generates a first bid for turn priority atthe intersection (e.g., utilizing the bid generation system 146). Thefirst bid is indicative of a first importance that autonomous vehicle 1100 traverses the intersection. The first bid is based uponcharacteristics of a trip of a passenger riding in the autonomousvehicle 1 100. Alternatively, when the autonomous vehicle 1 100 is nottransporting a passenger, the first bid may be based solely oncharacteristics of a trip of the autonomous vehicle.

The characteristics of the trip of the passenger may include at leastone of an intended path of the autonomous vehicle 1 100 that creates ashortest distance of the trip, a priority of the autonomous vehicle 1100, a priority of the passenger, an amount of idle time the autonomousvehicle 1 100 has spent at the intersection, a ride quality experiencedby the passenger, a total time of the trip, an expected arrival time ofthe autonomous vehicle 1 100 at a destination, previous approval ratingsof an autonomous vehicle service that is responsible for the autonomousvehicle 1 100, energy consumption of the autonomous vehicle 1 100, fuelconsumption of the autonomous vehicle 1 100, remaining distance left onthe trip, or a type of the trip. In an embodiment, some or all of thecharacteristics of the trip may be weighted.

The autonomous vehicle N 101 also generates a second bid for turnpriority. The second bid is indicative of a second importance that theautonomous vehicle N 101 traverses the intersection. The second bid isbased upon second characteristics of a second trip of a second passengerriding in the autonomous vehicle N 101. The second characteristics maybe similar to the above-identified first characteristics, but are fromthe perspective of a trip of the second passenger riding in theautonomous vehicle N 101.

The computing system 112 of the autonomous vehicle 1 100 transmits thefirst bid to the networked computing system 130. The autonomous vehicleN 101 also transmits the second bid to the networked computing system130. The networked computing system 130 then receives the first bid andthe second bid. The networked computing system 130 may determine thatthe autonomous vehicle 1 100 and the autonomous vehicle N 101 are bothpresent at the intersection at a similar time based upon the first bidand the second bid. For instance, in an embodiment, the first bid maycomprise a first location of the autonomous vehicle 1 100 in the drivingenvironment and the second bid may comprises a second location of theautonomous vehicle N 101 in the driving environment. The networkedcomputing system 130 may determine that the autonomous vehicle 1 100 andthe autonomous vehicle N 101 are proximate to the intersection basedupon the first bid, a first time at which the networked computing system130 receives the first bid, the second bid, and a second time at whichthe networked computing system 130 receives the second bid. In anotherembodiment, the first bid may include an identifier for the autonomousvehicle 1 100 and the second bid may include an identifier for theautonomous vehicle N 101. The networked computing system 130 maydetermine that the autonomous vehicle 1 100 and the autonomous vehicle N101 are present at the intersection based upon the identifier for theautonomous vehicle 1 100 and the identifier for the autonomous vehicle N101.

The networked computing system 130 then determines a turn order (i.e.,which autonomous vehicle is to traverse the intersection first) of theautonomous vehicle 1 100 and the autonomous vehicle N 101 based upon thefirst bid and the second bid. More specifically, the networked computingsystem 130 may determine the turn order based upon the firstcharacteristics and the second characteristics. In an embodiment, eachof the first characteristics may be assigned one or more weights andeach of the second characteristics may be assigned one or more weights.The networked computing system 130 may sum the one or more weights togenerate a score that is indicative of the first importance that theautonomous vehicle 1 100 traverses the intersection. Alternatively, thecomputing system 112 of the autonomous vehicle 100 1 may assign theweights and generate the score.

In an example, the autonomous vehicle 1 100 arrives at the intersectionsubsequent to the autonomous vehicle N 101. The networked computingsystem 130 may determine that the first importance is greater than thesecond importance. As such, the turn order may indicate that theautonomous vehicle 1 100 is to traverse the intersection prior to theautonomous vehicle N 101 traversing the intersection. Thus, it is to beunderstood that the above-described bid process may result in a turnorder for the intersection that differs from conventional traffic rules.

In a scenario in which the plurality of sensor signals generated by thesensor systems 102-104 are indicative of a non-autonomous vehicle thatis in proximity to the intersection, the autonomous vehicle 1 100 mayfail to generate the first bid. Alternatively, the first bid may includean indication of the non-autonomous vehicle. The networked computingsystem 130 may then base the turn order upon times at which theautonomous vehicle 1 100, the autonomous vehicle N 101, and thenon-autonomous vehicle arrive at the intersection.

In another scenario, the networked computing system 130 determines thata first future path of the autonomous vehicle 1 100 and a second futurepath of the autonomous vehicle N 101 fail to intersect. As such, theturn order may indicate that the autonomous vehicle 1 100 and theautonomous vehicle N 101 may traverse the intersection concurrently.

The networked computing system 130 then transmits the turn order to theautonomous vehicle 1 100. Responsive to receiving the turn order, thecomputing system 112 of the autonomous vehicle 1 100 controls at leastone of the vehicle propulsion system 106, the braking system 108, or thesteering system 110 based upon the turn order. The networked computingsystem 130 also transmits the turn order to the autonomous vehicle N101. Responsive to receiving the turn order, the computing system of theautonomous vehicle N 101 controls at least one of a vehicle propulsionsystem, a braking system, or a steering system of the autonomous vehicleN 101 based upon the turn order.

In an example, the networked computing system 130 determines that firstimportance is less than the second importance, and as such the turnorder indicates that the autonomous vehicle 1 100 is to traverse theintersection subsequent to the autonomous vehicle N 101 traversing theintersection. As such, the computing system 112 of the autonomousvehicle 1 100 controls at least one of the vehicle propulsion system106, the braking system 108, or the steering system 110 such that theautonomous vehicle 1 100 traverses the intersection subsequent to theautonomous vehicle N 101 traversing the intersection. In anotherexample, the networked computing system 130 determines that firstimportance is greater than the second importance, and as such the turnorder indicates that the autonomous vehicle 1 100 is to traverse theintersection prior to the autonomous vehicle N 101 traversing theintersection. As such, the computing system 112 of the autonomousvehicle 1 100 controls at least one of the vehicle propulsion system106, the braking system 108, or the steering system 110 such that theautonomous vehicle 1 100 traverses the intersection prior to theautonomous vehicle N 101 traversing the intersection.

In an embodiment, prior to controlling at least one of the vehiclepropulsion system, the braking system, or the steering system based uponthe turn order, the autonomous vehicle 1 100 may transmit the first bidto the autonomous vehicle N 101. The autonomous vehicle 1 100 mayreceive the second bid from the autonomous vehicle N 101. The autonomousvehicle 1 100 and the autonomous vehicle N 101 may communicate with eachother to determine the turn order.

In an embodiment, the autonomous vehicle 1 100 and the autonomousvehicle N 101 may belong to an autonomous vehicle fleet that ismaintained by an autonomous vehicle service. In another embodiment, theautonomous vehicle 1 100 may belong to a first autonomous vehicle fleetmaintained by a first autonomous vehicle service and the autonomousvehicle N 101 may belong to a second autonomous vehicle fleet maintainedby a second autonomous vehicle service.

Now turning to FIG. 4, illustrated is an exemplary accumulated occupancygrid 400 for a given time. The accumulated occupancy grid 400 shown inFIG. 4 includes 10 rows and 10 columns of cells; however, it iscontemplated that the depicted grid size is provided for illustrationpurposes, and other grid sizes are intended to fall within the scope ofthe hereto appended claims. As noted above, the occupancy accumulatorsystem 142 can generate the accumulated occupancy grid 400 for the giventime based at least in part on the sensor signals outputted by thesensor systems 102-104 for the given time (e.g., sensor signalsoutputted by a lidar sensor system, a radar sensor system, a camerasensor system, etc.) or from prior data at a given geographicallocation.

In the exemplary accumulated occupancy grid 400, cells 402 cancorrespond to a location of a tracked object in the driving environment.Moreover, in the exemplary accumulated occupancy grid 400, cells 404 andcells 406 can correspond to locations of non-tracked objects in thesensor signals outputted by the sensor systems 102-104. For instance,the non-tracked objects can be static objects (e.g., non-moving objects)in the driving environment surrounding the autonomous vehicle 1 100.Accordingly, the cells 404 and the cells 406 corresponding to thenon-tracked objects can remain in the accumulated occupancy grid 400.

As noted above, the occupancy accumulator system 142 can generate theaccumulated occupancy grid 400 based on prior map data. The prior mapdata, for instance, can specify that cells 408 in the accumulatedoccupancy grid 400 correspond to an undrivable area in the drivingenvironment surrounding the autonomous vehicle 1 100 (e.g., the cells408 can correspond to a location of a sidewalk, a median, or the like).Moreover, the occupancy accumulator system 142 can detect an unknownarea in the driving environment surrounding the autonomous vehicle 1 100based on the sensor signals outputted by the sensor systems 102-104 forthe given time. For instance, the cells 410 in the accumulated occupancygrid 400 can correspond to an unknown area in the driving environment.Furthermore, an orientation of the cells 412 in the occupancy grid 400can indicate a proposed orientation of the autonomous vehicle 1 100 inthe driving environment corresponding to the occupancy grid 400.

With reference to the occlusion aid system 144, a query depicted in FIG.4 includes a box 414 that has cells 410. The query can be performed toidentify whether any of the cells 414 either overlap cells correspondingto tracked objects (including the autonomous vehicle N 101), non-trackedobjects, or undrivable areas. If it is determined by the occlusion aidsystem 144 to be the autonomous vehicle N 101, then the bid generationsystem 146 described above may activate, since both the autonomousvehicle 1 100 and the autonomous vehicle N 101 are located proximate toan intersection 416.

With reference to FIG. 5A, a driving environment 500 is illustrated,where autonomous vehicles 501, 503 are about to enter into anintersection 504. The autonomous vehicle 501 and the autonomous vehicle503 may comprise components similar or identical to the autonomousvehicle 1 100 described above. The autonomous vehicle 501 has enteredinto proximity 510 of the intersection 504 (e.g. 100 ft, 50 ft, 10 ft)and has detected the autonomous vehicle 503 based upon sensor signalsoutput by sensor systems of the autonomous vehicle 501 (Step 1). Theautonomous vehicle 503 has also entered into proximity 513 of theintersection 504 and detected autonomous vehicle 501 based on sensorsignals output by sensor systems of the autonomous vehicle 503. (Step2). The driving environment also includes a vehicle 505. As describedabove, when vehicle 505 is non-autonomous, then the autonomous vehicles501, 503 will not submit bids for turn order, but will comply with theconventional “right of way” rules of the road, e.g. first to enter theintersection will go first, etc.

If vehicle 505 is an autonomous vehicle and has reached a predeterminedproximity from the intersection so as to be detected by the other twoautonomous vehicles 501, 503, then each of the autonomous vehicle 501,503, 505 will transmit bids to the networked computing system 130 (Step3). The bids are based upon characteristics of trips of passengers(described above) riding in the autonomous vehicles 501, 503, 505. Theautonomous vehicles 501, 503, 505 may assign the characteristicsnumerical values and may sum the numerical values to include in thebids. In an embodiment, certain characteristics may be ignored and hencesome characteristics may be assigned a numerical value of 0. Theautonomous vehicles 501, 503, 505 may then transmit their respectivebids (e.g., a first bid for the autonomous vehicle 501, a second bid forthe autonomous vehicle 503, and a third bid for the autonomous vehicle505) to the networked computing system 130.

The networked computing system 130 receives the bids from the autonomousvehicles 501, 503, 505. The networked computing system 130 determines aturn order based on the bids using a process similar to that describedabove (Step 4). The networked computing system 130 then transmits theturn order to each of the autonomous vehicles 501, 503, 505.

The autonomous vehicles 501, 503, 505 then receive the turn order fromthe networked computing system 130 and each of the autonomous vehicles501, 503, 505 traverse the intersection 504 according to the turn order(Step 5). In the case of the autonomous vehicle 501 and the autonomousvehicle 505, their intended paths do not meet. Their “turn” may beconsidered equal and therein, the turn order may dictate that they go atthe same time. In the illustrated example, autonomous vehicle 501 wouldmaintain a straight heading and autonomous vehicle 505 would execute aright-hand turn. This would be followed by autonomous vehicle 503executing a left-hand turn. It is envisioned that further lane maneuversmay be accomplished, such as lane changes during the intersection tomaneuver around another autonomous vehicle.

With reference to FIG. 5B, a similar driving environment 500′ toenvironment 500 is illustrated, but in this example, the autonomousvehicle 501 has an obscured portion 507 in an occupancy grid and hencecannot perceive the vehicle 505. This may be due to there being anocclusion 502. The driving environment 500′ includes an intersection504, the autonomous vehicles 501, 503, and 505, and the obscured portion507.

The autonomous vehicle 501 generates an occupancy grid (Step 1). Theoccupancy grid of the autonomous vehicle 501 includes an obscuredsection 507 (i.e., a blind spot) (Step 2). The obscured section 507 maybe hiding a vehicle 505 (illustrated in dashed lines). Since theautonomous vehicle 501 cannot perceive objects obfuscated by theocclusion 502, the autonomous vehicle 501 may transmit a query (Step 3)to the networked computing system 130 (not shown) in an indirectapproach or the autonomous vehicle 501 may transmit the query to theautonomous vehicle 503 in a direct approach. When the autonomous vehicle501 transmits the query to the networked computing system 130, then thenetworked computing system 130 determines an appropriate autonomousvehicle (e.g., the autonomous vehicle 503) to provide sensor signals 512that the autonomous vehicle 501 may utilize to “fill in” the obscuredsection 507.

In an illustrated example, the networked computing system 130 selectsthe autonomous vehicle 503 to submit the sensor signals 512 to theautonomous vehicle 501 (Step 4). If the autonomous vehicle 501 broadcastthe query in an omni-directional fashion, then the autonomous vehicle503 receives the query and transmits the sensor signals 512 to theautonomous vehicle 501. It is also envisioned that the autonomousvehicle 503 may submit its sensor signals 512 to the networked computingsystem 130, which in turn transmits the sensor signals 512 to theautonomous vehicle 501 and/or the autonomous vehicle 505.

Although the bid process and the sensor occlusion process describedabove are described as being executed by the networked computing system130, it is to be understood that these processes may be performed bydifferent computing systems.

FIGS. 6-8 illustrate exemplary methodologies relating to bid generationand turn order determination for an autonomous vehicle. While themethodologies are shown and described as being a series of acts that areperformed in a sequence, it is to be understood and appreciated that themethodologies are not limited by the order of the sequence. For example,some acts can occur in a different order than what is described herein.In addition, an act can occur concurrently with another act. Further, insome instances, not all acts may be required to implement a methodologydescribed herein.

Moreover, the acts described herein may be computer-executableinstructions that can be implemented by one or more processors and/orstored on a computer-readable medium or media. The computer-executableinstructions can include a routine, a sub-routine, programs, a thread ofexecution, and/or the like. Still further, results of acts of themethodologies can be stored in a computer-readable non-transitorymedium, displayed on a display device, and/or the like.

FIG. 6 illustrates a methodology 600 executed by an autonomous vehiclefor generating a bid for turn priority at an intersection. Themethodology 600 begins at 602, and at 604 the autonomous vehicledetermines that the autonomous vehicle and a second autonomous vehicleare proximate to an intersection based upon a plurality of sensorsignals generated by a plurality of sensor systems of the autonomousvehicle. At 606, the autonomous vehicle generates a first bid for turnpriority at the intersection. The first bid is indicative of a firstimportance that the autonomous vehicle traverses the intersection. Thefirst bid is based upon characteristics of a trip of a passenger ridingin the autonomous vehicle.

At 608, the autonomous vehicle transmits the first bid to a networkedcomputing system. The networked computing system determines a turn orderof the autonomous vehicle and the second autonomous vehicle based uponthe first bid and a second bid generated by the second autonomousvehicle. The second bid is indicative of a second importance that thesecond autonomous vehicle traverses the intersection. The networkedcomputing system transmits the turn order to the autonomous vehicle andthe second autonomous vehicle. At 610, responsive to receiving the turnorder from the networked computing system, the autonomous vehiclecontrols at least one of a vehicle propulsion system, a braking system,or a steering system of the autonomous vehicle based upon the turnorder. The methodology 600 concludes at 612.

FIG. 7 illustrates a methodology 700 executed by an autonomous vehiclefor removing an occlusion (i.e., a blind spot) from an occupancy grid.The methodology 700 begins at 702, and at 704, the autonomous vehiclegenerates an occupancy grid for a driving environment surrounding theautonomous vehicle for a given time. At 706, the autonomous vehicledetermines that an occlusion exists that blocks a field of view of theautonomous vehicle. In a non-limiting example, the occlusion can be abuilding, a parked car, or a material that reflects radar.

At 708, the autonomous vehicle transmits a query for sensor signals forthe occlusion in the occupancy grid to a networked computing system. Thequery includes an indication of the occlusion in the occupancy grid thatis in question and needs to be filled in with other sensory data. Thenetworked computing system gathers the sensor signals from a secondautonomous vehicle. At 710, the autonomous vehicle receives the sensorsignals from the networked computing system, wherein the sensor signalsenable a complete occupancy grid to be generated. The methodology 700concludes at 712.

Turning now to FIG. 8, illustrated is a methodology 800 executed by anetworked computing system for generating a turn order from bidsreceived from multiple autonomous vehicles. The methodology 800 beginsat 802, and at 804, the networked computing system receives a first bidgenerated by a first autonomous vehicle. The first bid is indicative ofa first importance that the first autonomous vehicle traverses anintersection. The first bid is based upon first characteristics of afirst trip of a first passenger riding in the first autonomous vehicle.At 806, the networked computing system receives a second bid generatedby a second autonomous vehicle. The second bid is indicative of a secondimportance that the second autonomous vehicle traverses theintersection. The second bid is based upon second characteristics of asecond trip of a second passenger riding in the second autonomousvehicle.

At 808, the networked computing system determines that the firstautonomous vehicle and the second autonomous vehicle are both present atthe intersection based upon the first bid and the second bid. At 810,the networked computing system determines a turn order for the firstautonomous vehicle and the second autonomous vehicle at the intersectionbased upon the first bid and the second bid. The turn order indicatesthat the first autonomous vehicle is to traverse the intersection priorto the second autonomous vehicle traversing the intersection or that thefirst autonomous vehicle is to traverse the intersection subsequent tothe second autonomous vehicle traversing the intersection.

At 812, the networked computing system transmits the turn order to thefirst autonomous vehicle, wherein the first autonomous vehicle operatesbased upon the turn order. At 814, the networked computing systemtransmits the turn order to the second autonomous vehicle, wherein thesecond autonomous vehicle operates based upon the turn order. Themethodology 800 concludes at 816.

Referring now to FIG. 9, a high-level illustration of an exemplarycomputing device 900 that can be used in accordance with the systems andmethodologies disclosed herein is illustrated. For instance, thecomputing device 900 may be or include the computing system 112 or thenetworked computing system 130. The computing device 900 includes atleast one processor 902 that executes instructions that are stored in amemory 904. The instructions may be, for instance, instructions forimplementing functionality described as being carried out by one or moresystems discussed above or instructions for implementing one or more ofthe methods described above.

The processor 902 may be a GPU, a plurality of GPUs, a CPU, a pluralityof CPUs, a multi-core processor, etc. The processor 902 may access thememory 904 by way of a system bus 906. In addition to storing executableinstructions, the memory 904 may also store bids, occupancy gridmovie(s), occupancy grids, cached query objects, predicted objects,accumulated occupancy grid(s), and so forth.

The computing device 900 additionally includes a data store 908 that isaccessible by the processor 902 by way of the system bus 906. The datastore 908 may include executable instructions, bids, prior occupancygrids, predicted objects, ignored static objects, accumulated occupancygrids, etc.

The computing device 900 also includes an input interface 910 thatallows inputs from a user to be received as instructions.

The computing device 900 also includes a network interface 914 thatinterfaces with a network. The network may include multiple computingdevices in communication with on another. Network interface 914 may usevarious wired and wireless connection protocols such as, direct connect,Ethernet, Bluetooth®, IEEE 1394a-b, serial, universal serial bus (USB),Digital Visual Interface (DVI), 802.11a/b/g/n/x, cellular, miracast, andthe like.

The network interface 914 allows external devices to communicate withthe computing device 900. For instance, the input interface 910 may beused to receive instructions from an external computer device, etc. Thenetwork interface 914 creates a secure way of doing such.

The computing device 900 also includes an output interface 912 thatinterfaces the computing device 900 with one or more external devices.For example, the computing device 900 may transmit control signals tothe vehicle propulsion system 106, the braking system 108, and/or thesteering system 110 by way of the output interface 912.

Additionally, while illustrated as a single system, it is to beunderstood that the computing device 900 may be a distributed system.Thus, for instance, several devices may be in communication by way of anetwork connection and may collectively perform tasks described as beingperformed by the computing device 900.

Various functions described herein can be implemented in hardware,software, or any combination thereof. If implemented in software, thefunctions can be stored on or transmitted over as one or moreinstructions or code on a computer-readable non-transitory medium.Computer-readable media includes computer-readable storage media. Acomputer-readable storage media can be any available storage media thatcan be accessed by a computer. By way of example, and not limitation,such computer-readable storage media can comprise RAM, ROM, EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to storedesired program code in the form of instructions or data structures andthat can be accessed by a computer. Disk and disc, as used herein,include compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk, and Blu-ray disc (BD), where disks usuallyreproduce data magnetically and discs usually reproduce data opticallywith lasers. Further, a propagated signal is not included within thescope of computer-readable storage media. Computer-readable media alsoincludes communication media including any medium that facilitatestransfer of a computer program from one place to another. A connection,for instance, can be a communication medium. For example, if thesoftware is transmitted from a website, server, or other remote sourceusing a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared, radio,and microwave, then the coaxial cable, fiber optic cable, twisted pair,DSL, or wireless technologies such as infrared, radio and microwave areincluded in the definition of communication medium. Combinations of theabove should also be included within the scope of computer-readablemedia.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Application-specific Integrated Circuits (ASICs),Application-specific Standard Products (ASSPs), System-on-a-chip systems(SOCs), Complex Programmable Logic Devices (CPLDs), etc.

What has been described above includes examples of one or moreembodiments. It is, of course, not possible to describe everyconceivable modification and alteration of the above devices ormethodologies for purposes of describing the aforementioned aspects, butone of ordinary skill in the art can recognize that many furthermodifications and permutations of various aspects are possible.Accordingly, the described aspects are intended to embrace all suchalterations, modifications, and variations that fall within the scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the details description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

What is claimed is:
 1. An autonomous vehicle, comprising: a plurality ofsensor systems that generate a plurality of sensor signals, wherein theplurality of sensor signals are indicative of a driving environment ofthe autonomous vehicle, wherein the driving environment includes anintersection and a second autonomous vehicle; a vehicle propulsionsystem; a braking system; a steering system; and a computing system thatis in communication with the plurality of sensor systems, the vehiclepropulsion system, the braking system, and the steering system, whereinthe computing system comprises: a processor; and memory that storescomputer-executable instructions that, when executed by the processor,cause the processor to perform acts comprising: determining that theautonomous vehicle and the second autonomous vehicle are proximate tothe intersection based upon the plurality of sensor signals; generatinga first bid for turn priority at the intersection, wherein the first bidis indicative of a first importance that the autonomous vehicletraverses the intersection, wherein the first bid is based uponcharacteristics of a trip of a passenger riding in the autonomousvehicle; transmitting the first bid to the networked computing system,wherein the networked computing system determines a turn order of theautonomous vehicle and the second autonomous vehicle based upon thefirst bid and a second bid generated by the second autonomous vehicle,wherein the second bid is indicative of a second importance that thesecond autonomous vehicle traverses the intersection, wherein thenetworked computing system transmits the turn order to the computingsystem of the autonomous vehicle; and responsive to receiving the turnorder from the networked computing system, controlling at least one ofthe vehicle propulsion system, the braking system, or the steeringsystem based upon the turn order.
 2. The autonomous vehicle of claim 1,wherein the characteristics of the trip of the passenger riding in theautonomous vehicle include at least one of: an intended path creating ashortest distance of the trip, a priority of the autonomous vehicle, apriority of the passenger, an amount of idle time of the autonomousvehicle at the intersection, ride quality experienced by the passenger,a total time of the trip of the passenger, an expected arrival time ofthe autonomous vehicle at a destination, previous approval ratings of anautonomous vehicle service responsible for the autonomous vehicle, fuelconsumption of the autonomous vehicle, energy consumption of theautonomous vehicle, remaining distance left for the autonomous vehicleto travel on the trip, time remaining in the trip, or a type of thetrip.
 3. The autonomous vehicle of claim 2, wherein each characteristicin the characteristics is assigned a weight, wherein the computingsystem sums the weights to generate a score that is indicative of thefirst importance that the autonomous vehicle traverses the intersection.4. The autonomous vehicle of claim 1, wherein the plurality of sensorsignals are further indicative of a non-autonomous vehicle that is inproximity to the intersection, wherein the first bid further includes anindication of the non-autonomous vehicle, wherein the turn order isbased upon times at which the autonomous vehicle, the second autonomousvehicle, and the non-autonomous vehicle arrive at the intersection. 5.The autonomous vehicle of claim 1, wherein the networked computingsystem determines that a first future path of the autonomous vehicle anda second future path of the second autonomous vehicle fail to intersect,wherein the turn order indicates that the autonomous vehicle and thesecond autonomous vehicle are to traverse the intersection concurrently.6. The autonomous vehicle of claim 1, the acts further comprising: priorcontrolling at least one of the vehicle propulsion system, the brakingsystem, or the steering system based upon the turn order, transmittingthe first bid to the second autonomous vehicle; receiving the second bidfrom the networked computing system; and communicating with the secondautonomous vehicle in order to determine the turn order.
 7. Theautonomous vehicle of claim 1, wherein the plurality of sensor systemsincludes at least one of: a radar sensor system; a lidar sensor system;or a camera sensor system.
 8. The autonomous vehicle of claim 1, whereinthe networked computing system determines that the first importance isless than the second importance, wherein the turn order indicates thatthe autonomous vehicle is to traverse the intersection subsequent to thesecond autonomous vehicle traversing the intersection, whereincontrolling at least one of the vehicle propulsion system, the brakingsystem, or the steering system based upon the turn order comprisescausing the autonomous vehicle to traverse the intersection subsequentto the second autonomous vehicle traversing the intersection.
 9. Theautonomous vehicle of claim 1, wherein the networked computing systemdetermines that the first importance is greater than the secondimportance, wherein the turn order indicates that the autonomous vehicleis to traverse the intersection prior to the second autonomous vehicletraversing the intersection, wherein controlling at least one of thevehicle propulsion system, the braking system, or the steering systembased upon the turn order comprises causing the autonomous vehicle totraverse the intersection prior to the second autonomous vehicletraversing the intersection.
 10. The autonomous vehicle of claim 1,wherein the autonomous vehicle belongs to a first autonomous vehiclefleet that is maintained by a first autonomous vehicle service, whereinthe second autonomous vehicle belongs to a second autonomous vehiclefleet that is maintained by a second autonomous vehicle service.
 11. Theautonomous vehicle of claim 1, wherein the autonomous vehicle arrives atthe intersection subsequent to the second autonomous vehicle arriving atthe intersection, wherein the networked computing system determines thatthe first importance is greater than the second importance, wherein theturn order indicates that the autonomous vehicle is to traverse theintersection prior to the second autonomous vehicle traversing theintersection.
 12. The autonomous vehicle of claim 1, wherein the firstbid comprises a first location of the autonomous vehicle in the drivingenvironment, wherein the second bid comprises a second location of theautonomous vehicle in the driving environment, wherein the networkedcomputing system determines that the autonomous vehicle and the secondautonomous vehicle are proximate to the intersection based upon thefirst bid, a first time at which the networked computing system receivesthe first bid, the second bid, and a second time at which the networkedcomputing system receives the second bid.
 13. A method performed by aprocessor of a networked computing system, the method comprising:receive a first bid generated by a first autonomous vehicle, wherein thefirst bid is indicative of a first importance that the first autonomousvehicle traverses an intersection, wherein the first bid is based uponfirst characteristics of a first trip of a first passenger riding in thefirst autonomous vehicle; receive a second bid generated by a secondautonomous vehicle, wherein the second bid is indicative of a secondimportance that the second autonomous vehicle traverses theintersection, wherein the second bid is based upon secondcharacteristics of a second trip of a second passenger riding in thesecond autonomous vehicle; determining that the first autonomous vehicleand the second autonomous vehicle are both present at the intersectionbased upon the first bid and the second bid; determining a turn orderfor the first autonomous vehicle and the second autonomous vehicle atthe intersection based upon the first bid and the second bid, whereinthe turn order indicates that the first autonomous vehicle is totraverse the intersection prior to the second autonomous vehicletraversing the intersection or that the first autonomous vehicle is totraverse the intersection subsequent to the second autonomous vehicletraversing the intersection; transmitting the turn order to the firstautonomous vehicle, wherein the first autonomous vehicle operates basedupon the turn order; and transmitting the turn order to the secondautonomous vehicle, wherein the second autonomous vehicle operates basedupon the turn order.
 14. The method of claim 13, wherein the firstautonomous vehicle arrives at the intersection subsequent to the secondautonomous vehicle arriving at the intersection, wherein the networkedcomputing system determines that the first importance is greater thanthe second importance, wherein the turn order indicates that the firstautonomous vehicle is to traverse the intersection prior to the secondautonomous vehicle traversing the intersection.
 15. The method of claim13, wherein the autonomous vehicle belongs to a first autonomous vehiclefleet that is maintained by a first autonomous vehicle service, whereinthe second autonomous vehicle belongs to a second autonomous vehiclefleet that is maintained by a second autonomous vehicle service.
 16. Themethod of claim 13, wherein the first characteristics include at leastone of: a first intended path creating a shortest distance of the firsttrip, a priority of the first autonomous vehicle, a first priority ofthe first passenger, a first amount of idle time of the first autonomousvehicle at the intersection, a ride quality experienced by the firstpassenger, a total time of the first trip of the first passenger, anexpected arrival time of the first autonomous vehicle at a firstdestination of the first trip, previous approval ratings of a firstautonomous vehicle service responsible for the first autonomous vehicle,fuel consumption of the first autonomous vehicle, energy consumption ofthe first autonomous vehicle, remaining distance left for the firstautonomous vehicle to travel on the first trip, time remaining in thefirst trip, or a type of the first trip, wherein the secondcharacteristics include at least one of: a second intended path creatinga shortest distance of the second trip, a priority of the secondautonomous vehicle, a second priority of the second passenger, a secondamount of idle time of the second autonomous vehicle at theintersection, a ride quality experienced by the second passenger, atotal time of the second trip of the second passenger, an expectedarrival time of the second autonomous vehicle at a second destination ofthe second trip, previous approval ratings of a second autonomousvehicle service responsible for the second autonomous vehicle, fuelconsumption of the second autonomous vehicle, energy consumption of thesecond autonomous vehicle, remaining distance left for the secondautonomous vehicle to travel on the second trip, time remaining in thesecond trip, or a type of the second trip.
 17. A computer-readablestorage medium comprising instructions that, when executed by aprocessor of a networked computing system, cause the processor toperform acts comprising: receive a first bid generated by a firstautonomous vehicle, wherein the first bid is indicative of a firstimportance that the first autonomous vehicle traverses an intersection,wherein the first bid is based upon first characteristics of a firsttrip of a first autonomous vehicle; receive a second bid generated by asecond autonomous vehicle, wherein the second bid is indicative of asecond importance that the second autonomous vehicle traverses theintersection, wherein the second bid is based upon secondcharacteristics of a second trip of a second autonomous vehicle;determining that the first autonomous vehicle and the second autonomousvehicle are both present at the intersection based upon the first bidand the second bid; determining a turn order for the first autonomousvehicle and the second autonomous vehicle at the intersection based uponthe first bid and the second bid, wherein the turn order indicates thatthe first autonomous vehicle is to traverse the intersection prior tothe second autonomous vehicle traversing the intersection or that thefirst autonomous vehicle is to traverse the intersection subsequent tothe second autonomous vehicle traversing the intersection; transmittingthe turn order to the first autonomous vehicle, wherein the firstautonomous vehicle operates based upon the turn order; and transmittingthe turn order to the second autonomous vehicle, wherein the secondautonomous vehicle operates based upon the turn order.
 18. Thecomputer-readable storage medium of claim 17, wherein the firstautonomous vehicle arrives at the intersection subsequent to the secondautonomous vehicle arriving at the intersection, wherein the networkedcomputing system determines that the first importance is greater thanthe second importance, wherein the turn order indicates that the firstautonomous vehicle is to traverse the intersection prior to the secondautonomous vehicle traversing the intersection.
 19. Thecomputer-readable storage medium of claim 17, wherein the first bidincludes a first identifier for the first autonomous vehicle, whereinthe second bid includes a second identifier for the second autonomousvehicle, wherein determining that the first autonomous vehicle and thesecond autonomous vehicle are both present at the intersection is basedupon the first identifier for the first autonomous vehicle and thesecond identifier for the second autonomous vehicle.
 20. Thecomputer-readable storage medium of claim 17, wherein the autonomousvehicle belongs to a first autonomous vehicle fleet that is maintainedby a first autonomous vehicle service, wherein the second autonomousvehicle belongs to a second autonomous vehicle fleet that is maintainedby a second autonomous vehicle service.